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SRLF: An Agent-Driven Set-Wise Reflective Learning Framework for Sequential Recommendation

Jiahao Wang, Bokang Fu, Yu Zhu, Yuli Liu

TL;DR

SRLF tackles the limitation of point-wise modeling in sequential recommendation by introducing a set-wise reflective learning framework that treats candidate item sets holistically. It leverages a closed-loop assess-validate-reflect cycle with a Set-wise Assessment Agent and an overlapping-set partitioning strategy to capture inter-item dynamics and alignment with user preferences, guided by a set-wise mismatch loss that triggers dual-path reflection updating the user profile and item semantics. Empirical results on CDs and MovieLens show state-of-the-art performance, validating the benefit of set-wise reasoning for capturing higher-order relational patterns in user behavior. The framework promises more transparent, adaptable, and effective recommendations in dynamic, multi-item contexts, with future work aimed at diversity and computational efficiency.

Abstract

LLM-based agents are emerging as a promising paradigm for simulating user behavior to enhance recommender systems. However, their effectiveness is often limited by existing studies that focus on modeling user ratings for individual items. This point-wise approach leads to prevalent issues such as inaccurate user preference comprehension and rigid item-semantic representations. To address these limitations, we propose the novel Set-wise Reflective Learning Framework (SRLF). Our framework operationalizes a closed-loop "assess-validate-reflect" cycle that harnesses the powerful in-context learning capabilities of LLMs. SRLF departs from conventional point-wise assessment by formulating a holistic judgment on an entire set of items. It accomplishes this by comprehensively analyzing both the intricate interrelationships among items within the set and their collective alignment with the user's preference profile. This method of set-level contextual understanding allows our model to capture complex relational patterns essential to user behavior, making it significantly more adept for sequential recommendation. Extensive experiments validate our approach, confirming that this set-wise perspective is crucial for achieving state-of-the-art performance in sequential recommendation tasks.

SRLF: An Agent-Driven Set-Wise Reflective Learning Framework for Sequential Recommendation

TL;DR

SRLF tackles the limitation of point-wise modeling in sequential recommendation by introducing a set-wise reflective learning framework that treats candidate item sets holistically. It leverages a closed-loop assess-validate-reflect cycle with a Set-wise Assessment Agent and an overlapping-set partitioning strategy to capture inter-item dynamics and alignment with user preferences, guided by a set-wise mismatch loss that triggers dual-path reflection updating the user profile and item semantics. Empirical results on CDs and MovieLens show state-of-the-art performance, validating the benefit of set-wise reasoning for capturing higher-order relational patterns in user behavior. The framework promises more transparent, adaptable, and effective recommendations in dynamic, multi-item contexts, with future work aimed at diversity and computational efficiency.

Abstract

LLM-based agents are emerging as a promising paradigm for simulating user behavior to enhance recommender systems. However, their effectiveness is often limited by existing studies that focus on modeling user ratings for individual items. This point-wise approach leads to prevalent issues such as inaccurate user preference comprehension and rigid item-semantic representations. To address these limitations, we propose the novel Set-wise Reflective Learning Framework (SRLF). Our framework operationalizes a closed-loop "assess-validate-reflect" cycle that harnesses the powerful in-context learning capabilities of LLMs. SRLF departs from conventional point-wise assessment by formulating a holistic judgment on an entire set of items. It accomplishes this by comprehensively analyzing both the intricate interrelationships among items within the set and their collective alignment with the user's preference profile. This method of set-level contextual understanding allows our model to capture complex relational patterns essential to user behavior, making it significantly more adept for sequential recommendation. Extensive experiments validate our approach, confirming that this set-wise perspective is crucial for achieving state-of-the-art performance in sequential recommendation tasks.

Paper Structure

This paper contains 16 sections, 5 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Sequential Recommendation Models.
  4. Agent-based Recommender Systems.
  5. Temporal Sets Prediction.
  6. Methodology
  7. Preliminaries
  8. Set-wise Assessment Agent
  9. Validation via Set-wise Mismatch Loss
  10. Dual-Path Reflective Learning
  11. Overall Algorithmic Procedure of SRLF
  12. Experiments
  13. Experimental Setup
  14. Overall Performance
  15. Ablation Study
  16. ...and 1 more sections

Figures (1)

  • Figure 1: The Set-wise Reflective Learning Framework (SRLF). The framework introduces a closed-loop learning mechanism. It begins with a set-wise assessment of items by an LLM agent. Mismatches with user feedback then trigger a dual-path reflection, which concurrently refines both the user profile and item semantics for continuous adaptation.